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- 저자 : Kezhi Liu (검색결과 3 건)
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( Peng Liu ),( Gaochao Xu ),( Kun Yang ),( Kezhi Wang ),( Yang Li ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.12
Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.
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Energy-Aware Preferential Attachment Model for Wireless Sensor Networks with Improved Survivability
( Rufei Ma ),( Erwu Liu ),( Rui Wang ),( Zhengqing Zhang ),( Kezhi Li ),( Chi Liu ),( Ping Wang ),( Tao Zhou ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.7
Recent years have witnessed a dramatic increase in topology research of wireless sensor networks (WSNs) where both energy consumption and survivability need careful consideration. To balance energy consumption and ensure survivability against both random failures and deliberate attacks, we resort to complex network theory and propose an energy-aware preferential attachment (EPA) model to generate a robust topology for WSNs. In the proposed model, by taking the transmission range and energy consumption of the sensor nodes into account, we combine the characters of Erdos -Renyi (ER) model and Barabasi-Albert (BA) model in this new model and introduce tunable coefficients for balancing connectivity, energy consumption, and survivability. The correctness of our theoretic analysis is verified by simulation results. We find that the topology of WSNs built by EPA model is asymptotically power-law and can have different characters in connectivity, energy consumption, and survivability by using different coefficients. This model can significantly improve energy efficiency as well as enhance network survivability by changing coefficients according to the requirement of the real environment where WSNs deployed and therefore lead to a crucial improvement of network performance.
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Wanhong Peng,Qingyu Tan,Minglan Yu,Ping Wang,Tingting Wang,Jixiang Yuan,Dongmei Liu,Dechao Chen,Chaohua Huang,Youguo Tan,Kezhi Liu,Bo Xiang,Xuemei Liang 대한신경정신의학회 2021 PSYCHIATRY INVESTIGATION Vol.18 No.5
Objective Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs).Methods Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways.Results Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10<sup>-16</sup> and 1.09×10<sup>-13</sup>, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data.Conclusion It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.
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